Characterizing Massively Parallel Polymorphism

Abstract

GPU computing has matured to include advanced C++ programming features. As a result, complex applications can potentially benefit from the continued performance improvements made to contemporary GPUs with each new generation. Tighter integration between the CPU and GPU, including a shared virtual memory space, increases the usability of productive programming paradigms traditionally reserved for CPUs, like object-oriented programming. Programmers are no longer forced to restructure both their code and data for GPU acceleration. However, the implementation and performance implications of advanced C++ on massively multithreaded accelerators have not been well studied. In this paper, we study the effects of runtime polymorphism on GPUs. We first detail the implementation of virtual function calls in contemporary GPUs using microbenchmarking. We then propose Parapoly, the first open-source polymorphic GPU benchmark suite. Using Parapoly, we further characterize the overhead caused by executing dynamic dispatch on GPUs using massively scaled CPU workloads. Our characterization demonstrates that the optimization space for runtime polymorphism on GPUs is fundamentally different than for CPUs. Where indirect branch prediction and ILP extraction strategies have dominated the work on CPU polymorphism, GPUs are fundamentally limited by excessive memory system contention caused by virtual function lookup and register spilling. Using the results of our study, we enumerate several pitfalls when writing polymorphic code for GPUs and suggest several new areas of system and architecture research that can help alleviate overhead.